Triple
T5335405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baraza la Kiswahili Zanzibar |
E123813
|
entity |
| Predicate | languageVarietyFocus |
P41081
|
FINISHED |
| Object | Kiunguja |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Kiunguja | Statement: [Baraza la Kiswahili Zanzibar, languageVarietyFocus, Kiunguja]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageVarietyFocus Context triple: [Baraza la Kiswahili Zanzibar, languageVarietyFocus, Kiunguja]
-
A.
primaryLanguageVariety
chosen
Indicates the main dialect or specific variety of a language that an entity primarily uses.
-
B.
focusesOnLanguage
Indicates that an entity’s primary attention, activity, or content is directed toward language as its main subject or concern.
-
C.
linguisticVariant
Indicates that one linguistic form is an alternative version or expression of another within the same or closely related language context.
-
D.
languageVariant
Indicates that one language is a variant, dialect, or localized form of another language.
-
E.
languageDiversity
Indicates the degree to which multiple distinct languages are present and used within a given context or population.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69bd464b07f8819095aa76577c9829e4 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85af799081909ee60bfbb65149ee |
completed | March 20, 2026, 5:36 p.m. |
| PD | Predicate disambiguation | batch_69bd84583dbc819088a03e3afb30178c |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2 p.m.